Why Nvidia's Stock Price Soared Even After the AI Crash
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Conclusion: The sharp drop immediately after earnings was not a ‘selling opportunity’ but a ‘buying opportunity’. Nvidia fell temporarily after its earnings announcement but rebounded sharply afterward. The frenzy for AI investment has not cooled, and structural demand is supporting the stock price.
By reading this article, you will: – Understand the mechanism behind ‘why Nvidia stock fell sharply and then rebounded’ after earnings – Gain criteria for deciding whether to sell or hold AI-related stocks – Learn specific actions individual investors can take regarding ‘earnings anomalies’
The pattern of a sharp drop followed by an immediate rebound after earnings has been repeated with Nvidia, and past cases demonstrate this structure.
📌 How to interpret the ‘usual drama’ after earnings
There is a kind of “ritual” that occurs after Nvidia’s earnings announcement.
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Stock price rises on expectations before earnings
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Sharp drop immediately after the announcement due to ‘selling on the news’
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Rebound within a few days, aiming for new highs
I call this pattern the ‘Nvidia earnings anomaly.’ The problem is whether you can make the decision to ‘buy’ the moment it drops, even if you know about this anomaly. Most individual investors see the sharp drop and sell, thinking, ‘It’s over after all.’
In fact, sharp drops after earnings announcements have been repeated many times.
Nevertheless, Nvidia has continued to trend upward in the medium to long term.
This is nothing but because structural demand for AI is supporting the floor of the stock price.
💡 The real reason the AI fever ‘won’t cool down’
What many people overlook is the structural reason why the AI investment frenzy will not end as a ‘bubble’.
It is not mere speculation, but the following actual demand is accumulating:
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Data center investments by major cloud providers (Microsoft, Google, Amazon, etc.) are expanding year by year and form the core of GPU demand
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National AI strategies of various governments are creating industrial subsidies and procurement demand
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From startups to large corporations, they continue to pay for AI model training and inference costs
What should be noted is ‘who is paying Nvidia.’ It is not individuals, but some of the world’s largest tech companies that are the customers. In other words, Nvidia’s revenue is linked not to ‘consumer sentiment’ but to ‘corporate capital expenditure plans.’
Unlike consumer-facing businesses, corporate Capex (capital expenditure), once planned, does not easily stop even if the stock price fluctuates somewhat.
This is the basis for why the AI fever is described as “unabated.”
📌 Case Study 1: The “Expectation Problem” That Looks Like an Earnings Miss
The most common reason Nvidia’s stock price drops after earnings is not a “numerical miss.”
In many cases, the numbers themselves are at record highs.
The issue is the game of expectations: “Did it beat the market consensus forecast, and by how much?”
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Even if revenue is at an all-time high, if it falls short of Wall Street forecasts, “disappointment selling” occurs.
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Conversely, if it exceeds forecasts even slightly, “surprise buying” comes in.
I assert that “judging Nvidia’s earnings by the absolute value of the numbers is for amateurs.” What you really need to look at is the “growth rate of the data center division” and the “guidance for the next quarter.” If these are strong, temporary sharp drops turn into buying opportunities.
📊 Case Study 2: Why the “AI Bubble Burst” Theory Has Been Wrong Repeatedly
Since 2023, the theory that the AI stock bubble will burst has appeared many times.
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“Growth stocks will be sold off due to rising interest rates”
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“The ChatGPT boom is a passing fad”
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“Cheap Chinese AI models will steal the market”
However, while these points temporarily shook the stock price, they could not shake Nvidia’s core business.
Why? Because the demand for GPUs is determined not by “whether AI models are a hot topic,” but by “whether they are actually needed to train and infer models.”
What many people overlook is that “intensifying competition among AI models” is not necessarily negative for Nvidia. The more competing AI models there are, the greater the total demand for GPUs required for training. Ironically, the intensification of AI competition acts as a tailwind for Nvidia.
💰 Case Study 3: The Pattern Where Individual Investors Fail After Earnings
I will be honest here. Even among those around me, there are not a few people who “cut their losses” during the sharp drop after earnings and missed out on the subsequent rebound.
The typical failure pattern is as follows:
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Buying on anticipation before earnings (risk of buying at the peak)
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Sharp drop immediately after the announcement → panic selling
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Rebound the next day or a few days later
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Regret of “I should have held onto it after all”
The root cause of this failure is “judging Nvidia’s stock price movements based on news.”.
Nvidia’s stock price moves based on sentiment (market mood) in the short term. However, in the medium term, it is determined by backlog of data center GPU orders and guidance.
If you look at this, you will understand that a temporary sharp drop is not a “sign to escape” but a “chance to load up.”
📌 A perspective other media won’t write: Nvidia is no longer just a GPU company
What I want to emphasize here is a structural change that many analyses overlook.
Nvidia has already transcended the definition of a “GPU manufacturer.”
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CUDA (computing platform): With over 10 years of accumulation, the cost for AI developers to switch is extremely high
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Connection technologies like NVLink and InfiniBand: Functioning as the “nervous system” of large-scale AI clusters
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Software ecosystem: Providing not just hardware, but also inference optimization and model deployment tools
Here is my view: Nvidia is becoming an “AI infrastructure OS company” rather than a “semiconductor company.” Just as Microsoft dominated the market with its operating system, Nvidia is attempting to build a similar position in the AI computing foundation. From this perspective, the debate over the “fair value” of the stock price itself changes.
The existence of CUDA is particularly important.
The more code developers write in CUDA, the deeper their dependence on Nvidia’s GPUs becomes. This is a lock-in effect, and it is not easy for competitors (such as AMD and Intel) to break through.
📊 Speaking honestly about risks
Just praising Nvidia does not constitute an analysis. I will also honestly list the risks.
Export restrictions are particularly noteworthy.
Every time the U.S. government restricts GPU exports to China, Nvidia’s potential market is eroded. China was once an important market for Nvidia, but due to regulations, the situation continues where they can only respond with substitute products (such as the H20) (estimated).
We also cannot overlook customers developing their own chips. Google has its TPUs, Amazon has Trainium and Inferentia, and Microsoft is also developing its own chips. However, these are “complements” and not complete replacements for Nvidia. At least for the next 2 to 3 years, I believe Nvidia’s position is rock solid.
💰 The Structure of AI Investment: Who is Spending How Much
Regarding specific numbers, since there is no detailed data in the reference news articles, the following shows an overview widely reported in the industry (specific amounts are estimates/rounded values):
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Capex (capital expenditure) of major cloud companies continues to expand at a scale of tens of billions of dollars per year (estimated)
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A significant portion of that is said to be going toward GPU infrastructure
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Nvidia’s data center division has grown to account for the majority of its revenue (estimated)
It is unreasonable to think that this scale of investment will end as a “temporary boom.”
Companies reduce Capex when there is an economic recession or a technological substitution.
There are no signs of that at this moment.
📈 Practical Response to the Post-Earnings Plunge-and-Rebound Pattern
Knowing the theory is meaningless if you cannot act on it. From here, I will show a practical decision-making framework.
📌 What to do before earnings announcements
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Determine position size: Avoid going all-in before earnings. Diversify to prepare for the risk of a sharp drop
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Check the “bottom line” of guidance: Understand Wall Street consensus in advance
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Eliminate emotions: Nvidia’s earnings will always be a “drama.” Plan with that as a premise
📌 What to do immediately after earnings announcements
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Look at the “direction of guidance” rather than the absolute value of the numbers
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Prioritize checking the growth rate of the data center division
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Determine whether the cause of the sharp drop is a “deterioration in fundamentals” or an “issue with expectations”
📌 Message to medium- to long-term holders
There is one thing I can say from my more than 10 years of experience as an analyst: “The stock price of a company with a strong business model will rise while repeating short-term fluctuations.” Nvidia holds the core of AI infrastructure. Selling in a panic due to a short-term sharp drop can be the most expensive lesson you ever learn.
📝 Re-conclusion: The AI fever is a “structure,” not a “mood.”
Let’s summarize the discussion so far.
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Nvidia’s sharp drop after earnings followed by a rebound is a repeating pattern driven by structural demand.
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The reason the investment fever for AI does not cool down is that it is supported by corporate Capex, and it has nothing to do with consumer sentiment.
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Nvidia is evolving beyond a GPU manufacturer into an AI infrastructure platform company.
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Risks are real, but export restrictions and customers developing their own chips are the biggest variables.
I assert this: Every time Nvidia’s stock price drops temporarily after earnings and then rebounds, voices saying “AI is over” emerge. However, those voices have been wrong every single time. This is because demand for AI is driven by “necessity,” not “enthusiasm.”
✅ 3 Concrete Actions to Take Right Now
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Check the “Wall Street consensus forecast” before the next earnings announcement.
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Understand that the stock moves based on the “divergence from the forecast” rather than whether the numbers are good or bad, and use the reaction after the announcement as a basis for judgment.
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Regularly check the sales ratio of Nvidia’s data center division.
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If this starts to shrink, it is a sign of structural change. Until then, do not be misled by short-term sharp drops.
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Watch the US government’s movements regarding export restrictions once a month.
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This is Nvidia’s biggest risk. Be prepared to react immediately to news of tighter regulations.
⚠️ Investment Disclaimer: This article is for informational purposes only and does not constitute investment advice or recommendations. Please make investment decisions at your own risk and consult with a professional if necessary.